DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart

Today, we are excited to reveal that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek AI‘s first-generation frontier design, DeepSeek-R1, in addition to the distilled versions varying from 1.5 to 70 billion criteria to build, experiment, and responsibly scale your generative AI ideas on AWS.

In this post, we show how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to release the distilled versions of the designs also.

Overview of DeepSeek-R1

DeepSeek-R1 is a large language model (LLM) established by DeepSeek AI that utilizes support finding out to boost reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A key distinguishing function is its support learning (RL) step, which was used to improve the design’s actions beyond the standard pre-training and tweak process. By incorporating RL, DeepSeek-R1 can adapt more effectively to user feedback and objectives, ultimately boosting both importance and clarity. In addition, surgiteams.com DeepSeek-R1 employs a chain-of-thought (CoT) approach, meaning it’s equipped to break down intricate questions and factor through them in a detailed way. This guided reasoning procedure permits the model to produce more precise, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT capabilities, aiming to create structured responses while focusing on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has actually recorded the industry’s attention as a flexible text-generation design that can be incorporated into various workflows such as representatives, sensible reasoning and data interpretation tasks.

DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion specifications, making it possible for efficient inference by routing questions to the most pertinent specialist “clusters.” This method enables the design to focus on different problem domains while maintaining general efficiency. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances to release the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.

DeepSeek-R1 distilled designs bring the thinking capabilities of the main R1 design to more effective architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller, more effective models to mimic the behavior and thinking patterns of the larger DeepSeek-R1 model, utilizing it as a teacher design.

You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock . Because DeepSeek-R1 is an emerging design, we recommend releasing this design with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, avoid hazardous content, and assess designs against essential security criteria. At the time of writing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create numerous guardrails tailored to various usage cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing safety controls throughout your generative AI applications.

Prerequisites

To release the DeepSeek-R1 design, you require access to an ml.p5e circumstances. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and verify you’re utilizing ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are deploying. To request a limitation boost, develop a limitation increase request and connect to your account team.

Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) permissions to use Amazon Bedrock Guardrails. For directions, see Set up permissions to use guardrails for material filtering.

Implementing guardrails with the ApplyGuardrail API

Amazon Bedrock Guardrails enables you to introduce safeguards, prevent hazardous content, and examine designs against crucial safety requirements. You can carry out precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to assess user inputs and model actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.

The basic circulation involves the following steps: First, the system receives an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it’s sent out to the model for reasoning. After receiving the model’s output, another guardrail check is used. If the output passes this last check, it’s returned as the outcome. However, if either the input or output is intervened by the guardrail, yewiki.org a message is returned suggesting the nature of the intervention and whether it happened at the input or output stage. The examples showcased in the following sections demonstrate reasoning using this API.

Deploy DeepSeek-R1 in Amazon Bedrock Marketplace

Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized foundation designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:

1. On the Amazon Bedrock console, select Model catalog under Foundation designs in the navigation pane.
At the time of composing this post, you can utilize the InvokeModel API to conjure up the design. It does not support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a provider and choose the DeepSeek-R1 design.

The design detail page supplies necessary details about the design’s abilities, pricing structure, and application standards. You can discover detailed use instructions, including sample API calls and code bits for integration. The design supports different text generation jobs, including material development, code generation, and question answering, utilizing its support learning optimization and CoT thinking abilities.
The page likewise includes implementation alternatives and licensing details to assist you get going with DeepSeek-R1 in your applications.
3. To start utilizing DeepSeek-R1, choose Deploy.

You will be triggered to set up the implementation details for DeepSeek-R1. The design ID will be pre-populated.
4. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters).
5. For Number of circumstances, go into a number of instances (in between 1-100).
6. For Instance type, pick your circumstances type. For optimal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised.
Optionally, you can set up sophisticated security and infrastructure settings, including virtual personal cloud (VPC) networking, service function approvals, and file encryption settings. For the majority of utilize cases, the default settings will work well. However, for production releases, you may desire to review these settings to line up with your company’s security and compliance requirements.
7. Choose Deploy to start utilizing the design.

When the deployment is total, you can test DeepSeek-R1’s capabilities straight in the Amazon Bedrock play ground.
8. Choose Open in playground to access an interactive user interface where you can explore different triggers and adjust design parameters like temperature level and optimum length.
When utilizing R1 with Bedrock’s InvokeModel and Playground Console, use DeepSeek’s chat template for optimal outcomes. For instance, content for inference.

This is an outstanding way to check out the model’s thinking and text generation abilities before incorporating it into your applications. The play area provides immediate feedback, assisting you understand how the design responds to numerous inputs and letting you fine-tune your prompts for optimal results.

You can rapidly test the design in the playground through the UI. However, to conjure up the released design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.

Run inference utilizing guardrails with the released DeepSeek-R1 endpoint

The following code example demonstrates how to carry out inference utilizing a released DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have actually produced the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime customer, configures reasoning parameters, and sends a demand to produce text based on a user timely.

Deploy DeepSeek-R1 with SageMaker JumpStart

SageMaker JumpStart is an artificial intelligence (ML) center with FMs, integrated algorithms, and prebuilt ML options that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your information, and deploy them into production using either the UI or SDK.

Deploying DeepSeek-R1 design through SageMaker JumpStart offers 2 practical methods: utilizing the intuitive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let’s explore both approaches to help you pick the technique that finest matches your needs.

Deploy DeepSeek-R1 through SageMaker JumpStart UI

Complete the following actions to release DeepSeek-R1 using SageMaker JumpStart:

1. On the SageMaker console, select Studio in the navigation pane.
2. First-time users will be prompted to create a domain.
3. On the SageMaker Studio console, select JumpStart in the navigation pane.

The model internet browser displays available models, pediascape.science with details like the service provider name and model abilities.

4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card.
Each model card reveals key details, including:

– Model name
– Provider name
– Task category (for example, Text Generation).
Bedrock Ready badge (if applicable), suggesting that this model can be signed up with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to conjure up the design

5. Choose the design card to view the design details page.

The design details page includes the following details:

– The design name and supplier details.
Deploy button to release the model.
About and Notebooks tabs with detailed details

The About tab includes crucial details, such as:

– Model description.
– License details.
– Technical specs.
– Usage standards

Before you deploy the model, it’s recommended to evaluate the design details and license terms to validate compatibility with your usage case.

6. Choose Deploy to proceed with release.

7. For Endpoint name, utilize the automatically produced name or produce a custom one.
8. For example type ¸ select an instance type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, go into the number of circumstances (default: 1).
Selecting suitable circumstances types and counts is essential for cost and efficiency optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time inference is selected by default. This is optimized for sustained traffic and low latency.
10. Review all configurations for accuracy. For this model, we strongly recommend adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location.
11. Choose Deploy to release the model.

The implementation procedure can take a number of minutes to complete.

When deployment is complete, your endpoint status will change to InService. At this moment, the design is ready to accept reasoning requests through the endpoint. You can monitor the implementation progress on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the deployment is complete, you can invoke the model using a SageMaker runtime client and incorporate it with your applications.

Deploy DeepSeek-R1 using the SageMaker Python SDK

To get going with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the essential AWS permissions and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the design is offered in the Github here. You can clone the note pad and range from SageMaker Studio.

You can run additional demands against the predictor:

Implement guardrails and run reasoning with your SageMaker JumpStart predictor

Similar to Amazon Bedrock, you can also use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing the Amazon Bedrock console or the API, and execute it as shown in the following code:

Tidy up

To avoid unwanted charges, complete the steps in this section to tidy up your resources.

Delete the Amazon Bedrock Marketplace deployment

If you released the design using Amazon Bedrock Marketplace, complete the following actions:

1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace deployments.
2. In the Managed deployments area, locate the endpoint you wish to erase.
3. Select the endpoint, and on the Actions menu, pick Delete.
4. Verify the endpoint details to make certain you’re deleting the proper deployment: 1. Endpoint name.
2. Model name.
3. Endpoint status

Delete the SageMaker JumpStart predictor

The SageMaker JumpStart design you deployed will sustain expenses if you leave it running. Use the following code to erase the endpoint if you wish to stop sustaining charges. For more details, see Delete Endpoints and Resources.

Conclusion

In this post, we explored how you can access and deploy the DeepSeek-R1 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to start. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting going with Amazon SageMaker JumpStart.

About the Authors

Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative AI companies build ingenious solutions utilizing AWS services and accelerated calculate. Currently, he is concentrated on establishing techniques for fine-tuning and enhancing the reasoning performance of large language designs. In his downtime, Vivek enjoys treking, enjoying motion pictures, and trying various foods.

Niithiyn Vijeaswaran is a Generative AI Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS AI accelerators (AWS Neuron). He holds a Bachelor’s degree in Computer technology and Bioinformatics.

Jonathan Evans is an Expert Solutions Architect working on generative AI with the Third-Party Model Science team at AWS.

Banu Nagasundaram leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker’s artificial intelligence and generative AI hub. She is passionate about constructing services that assist customers accelerate their AI journey and unlock company worth.